375 - Computational and Laboratory Identification of Risk-Driving Alleles on Juvenile Idiopathic Arthritis (JIA)-Associated Haplotypes

Publication Number: 375.4412

Adam Y. He, Cornell University, Ithaca, NY, United States; hannah C Ainsworth, Wake Forest School of Medicine of Wake Forest Baptist Medical Center, Winston-Salem, NC, United States; Kaiyu Jiang, University of Washington School of Medicine, Seattle, WA, United States; Yanmin Chen, University of Washington School of Medicine, Seattle, WA, United States; Ekaterina S. Khvatkova, Wake Forest School of Medicine of Wake Forest Baptist Medical Center, Winston Salem, NC, United States; Carl D Langefeld, Wake Forest School of Medicine of Wake Forest Baptist Medical Center, Winston-Salem, NC, United States; Charles G. Danko, Cornell University, Ithaca, NY, United States; Jim Jarvis, University of Washington School of Medicine, Seattle, WA, United States

Background: Multiple genomic regions confer risk for JIA. However, identifying the SNPs that exert the biological effects that confer risk, and therefore the target genes, is confounded by linkage disequilibrium (LD). LD makes the true risk-driving SNPs statistically indistinguishable from those (the majority) with which they are co-inherited.

Objective: To use novel analytics and wet lab procedures to identify SNPs within JIA risk regions whose biological properties make them likely disease-driving variants.

Design/Methods: We developed independent methods for assessing the likelihood that a SNP on a disease haplotype exerts risk-driving biological effects. One is an AI-based approach that captures the effect of SNPs on each stage of enhancer assembly (as JIA risk variants are likely to be situated in enhancers). We trained sequence-to-activity models to predict three different types of genomic data, ATAC-seq, PRO-cap, and PRO-seq, that capture different stages of enhancer assembly. The second method predicts allelic effects on DNA shape/topology, a feature of DNA function that affects transcription factor binding. We undertook wet lab confirmation of computational predictions using enhancer reporter and DNA pulldown assays.

Results: Using the AI-based method for assessing enhancer (and transcriptional) effects, we identified a SNP within the JIA-associated IRF1 locus, rs2548998 G to A, as likely to influence transcription. This SNP lies within a large enhancer cluster upstream of the IRF1 gene, marked by H3K27ac ChIPseq and PROseq-dREG peaks. Using a luciferase enhancer reporter assay, we demonstrated that the rs2548998 G to A allele showed 2x more activity than the common allele in primary human CD4+ T cells (Figure 1). We used a DNA pulldown assay to corroborate predictions from DNA topology analyses regarding effects of SNPs’ effects on transcription factor binding. We tested 2 SNPs, rs4147359 G toA, within an a CREB1 binding site upstream of IL2RA (using nuclei extracted from Jurkat T cells), and rs7234029 A to G, situated in a GATA1 binding site within the first intron of PTPN2 (using myeloid K562 cells; GATA1 is not expressed in lymphoid cells). Figure 2 summarizes the results of these experiments. The SNP, rs4147359 G to A reduced CREB1 2-fold compared to the common allele (p < 0.01), while rs7234029 AG reduced GATA1 binding by more than 4-fold compared to the common allele (p < 0.05).

Conclusion(s): Using computational techniques that predict different features of DNA/chromatin, and corroborating computational predictions with wet lab techniques, we can identify true risk-driving alleles on JIA risk haplotypes.

Results of an enhancer reporter assay performed in CD3/CD28/IL2-activated primary humanCD4+ T cells
Fig 1 Enhancer reporter.pdfWe compared the common allele
(“rs2548998 WT”) with the G to A variant (“rs2548998 SNP”). The SNP showed 2-fold enhancer activity compared with the common allele. Raw data from each of 4 experiments (relative luciferase expression) is shown in the table on the right. The negative control consisted of the green fluorescence protein vector without reporter vector without the enhancer construct inserted


Identification of proteins with different binding affinity to common allele or allele of single nucleotide polymorphisms identified via DNA topology analyses as likely to have significant effects on transcription factor binding
Fig 2 DNA Pulldown.pdf(A) Western blot verification of DNA pull-down assay. Nuclear protein extracts from Jurkat cells were incubated with Streptavidin MicroBeads and biotin-labeled probes representing the common allele or allele of rs4147359 (GA, TF CREB1). Nuclear protein extracts from K562 cells were incubated with Streptavidin MicroBeads and biotin-labeled probes representing the common allele or allele of rs7234029 (AG, TF
GATA1). (B) The protein levels were quantified using the iBright Analysis System (Thermo Fisher Scientific). Significant differences were marked with an asterisk (*p < 0.05; **p < 0.01; n=4).